The localization of a mobile robot in a real environment is a complex task. In this paper, an algorithm that solves the global localization is presented. The proposal is based on a merit function that ranks different possible poses obtained from the acquired image, together with an iterative process for the minimization of that function using a particle filter. Landmark detection has been done with an omnidirectional camera pointing to the ceiling, combined with an infrared passband filter

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... extracts the lights. Several real experiments, both for global localization and the kidnapped robot problem, have been done in a museum. Results show a high accuracy, robustness, and real-time execution in this complex and crowded environment.